Druid can ingest denormalized data in JSON, CSV, or a delimited form such as TSV, or any custom format. While most examples in the documentation use data in JSON format, it is not difficult to configure Druid to ingest any other delimited data. We welcome any contributions to new formats.
For additional data formats, please see our extensions list.
The following samples show data formats that are natively supported in Druid:
JSON
{"timestamp": "2013-08-31T01:02:33Z", "page": "Gypsy Danger", "language" : "en", "user" : "nuclear", "unpatrolled" : "true", "newPage" : "true", "robot": "false", "anonymous": "false", "namespace":"article", "continent":"North America", "country":"United States", "region":"Bay Area", "city":"San Francisco", "added": 57, "deleted": 200, "delta": -143} {"timestamp": "2013-08-31T03:32:45Z", "page": "Striker Eureka", "language" : "en", "user" : "speed", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Australia", "country":"Australia", "region":"Cantebury", "city":"Syndey", "added": 459, "deleted": 129, "delta": 330} {"timestamp": "2013-08-31T07:11:21Z", "page": "Cherno Alpha", "language" : "ru", "user" : "masterYi", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"article", "continent":"Asia", "country":"Russia", "region":"Oblast", "city":"Moscow", "added": 123, "deleted": 12, "delta": 111} {"timestamp": "2013-08-31T11:58:39Z", "page": "Crimson Typhoon", "language" : "zh", "user" : "triplets", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"China", "region":"Shanxi", "city":"Taiyuan", "added": 905, "deleted": 5, "delta": 900} {"timestamp": "2013-08-31T12:41:27Z", "page": "Coyote Tango", "language" : "ja", "user" : "cancer", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"Japan", "region":"Kanto", "city":"Tokyo", "added": 1, "deleted": 10, "delta": -9}
CSV
2013-08-31T01:02:33Z,"Gypsy Danger","en","nuclear","true","true","false","false","article","North America","United States","Bay Area","San Francisco",57,200,-143 2013-08-31T03:32:45Z,"Striker Eureka","en","speed","false","true","true","false","wikipedia","Australia","Australia","Cantebury","Syndey",459,129,330 2013-08-31T07:11:21Z,"Cherno Alpha","ru","masterYi","false","true","true","false","article","Asia","Russia","Oblast","Moscow",123,12,111 2013-08-31T11:58:39Z,"Crimson Typhoon","zh","triplets","true","false","true","false","wikipedia","Asia","China","Shanxi","Taiyuan",905,5,900 2013-08-31T12:41:27Z,"Coyote Tango","ja","cancer","true","false","true","false","wikipedia","Asia","Japan","Kanto","Tokyo",1,10,-9
TSV (Delimited)
2013-08-31T01:02:33Z "Gypsy Danger" "en" "nuclear" "true" "true" "false" "false" "article" "North America" "United States" "Bay Area" "San Francisco" 57 200 -143 2013-08-31T03:32:45Z "Striker Eureka" "en" "speed" "false" "true" "true" "false" "wikipedia" "Australia" "Australia" "Cantebury" "Syndey" 459 129 330 2013-08-31T07:11:21Z "Cherno Alpha" "ru" "masterYi" "false" "true" "true" "false" "article" "Asia" "Russia" "Oblast" "Moscow" 123 12 111 2013-08-31T11:58:39Z "Crimson Typhoon" "zh" "triplets" "true" "false" "true" "false" "wikipedia" "Asia" "China" "Shanxi" "Taiyuan" 905 5 900 2013-08-31T12:41:27Z "Coyote Tango" "ja" "cancer" "true" "false" "true" "false" "wikipedia" "Asia" "Japan" "Kanto" "Tokyo" 1 10 -9
Note that the CSV and TSV data do not contain column heads. This becomes important when you specify the data for ingesting.
Druid supports custom data formats and can use the Regex parser or the JavaScript parsers to parse these formats. Please note that using any of these parsers for parsing data will not be as efficient as writing a native Java parser or using an external stream processor. We welcome contributions of new Parsers.
All forms of Druid ingestion require some form of schema object. The format of the data to be ingested is specified using theparseSpec entry in your dataSchema.
"parseSpec":{ "format" : "json", "timestampSpec" : { "column" : "timestamp" }, "dimensionSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } }
If you have nested JSON, Druid can automatically flatten it for you.
"parseSpec": { "format" : "csv", "timestampSpec" : { "column" : "timestamp" }, "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"], "dimensionsSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } }
If your input files contain a header, the columns field is optional and you don't need to set. Instead, you can set the hasHeaderRow field to true, which makes Druid automatically extract the column information from the header. Otherwise, you must set the columns field and ensure that field must match the columns of your input data in the same order.
Also, you can skip some header rows by setting skipHeaderRows in your parseSpec. If both skipHeaderRows and hasHeaderRow options are set, skipHeaderRows is first applied. For example, if you set skipHeaderRows to 2 and hasHeaderRow to true, Druid will skip the first two lines and then extract column information from the third line.
Note that hasHeaderRow and skipHeaderRows are effective only for non-Hadoop batch index tasks. Other types of index tasks will fail with an exception.
The columns field must be included and and ensure that the order of the fields matches the columns of your input data in the same order.
"parseSpec": { "format" : "tsv", "timestampSpec" : { "column" : "timestamp" }, "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"], "delimiter":"|", "dimensionsSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } }
Be sure to change the delimiter to the appropriate delimiter for your data. Like CSV, you must specify the columns and which subset of the columns you want indexed.
If your input files contain a header, the columns field is optional and you don't need to set. Instead, you can set the hasHeaderRow field to true, which makes Druid automatically extract the column information from the header. Otherwise, you must set the columns field and ensure that field must match the columns of your input data in the same order.
Also, you can skip some header rows by setting skipHeaderRows in your parseSpec. If both skipHeaderRows and hasHeaderRow options are set, skipHeaderRows is first applied. For example, if you set skipHeaderRows to 2 and hasHeaderRow to true, Druid will skip the first two lines and then extract column information from the third line.
Note that hasHeaderRow and skipHeaderRows are effective only for non-Hadoop batch index tasks. Other types of index tasks will fail with an exception.
The columns field must be included and and ensure that the order of the fields matches the columns of your input data in the same order.
"parseSpec":{ "format" : "regex", "timestampSpec" : { "column" : "timestamp" }, "dimensionsSpec" : { "dimensions" : [<your_list_of_dimensions>] }, "columns" : [<your_columns_here>], "pattern" : <regex pattern for partitioning data> }
The columns field must match the columns of your regex matching groups in the same order. If columns are not provided, default columns names (“column_1”, “column2”, ... “column_n”) will be assigned. Ensure that your column names include all your dimensions.
"parseSpec":{ "format" : "javascript", "timestampSpec" : { "column" : "timestamp" }, "dimensionsSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] }, "function" : "function(str) { var parts = str.split(\"-\"); return { one: parts[0], two: parts[1] } }" }
Note with the JavaScript parser that data must be fully parsed and returned as a {key:value} format in the JS logic. This means any flattening or parsing multi-dimensional values must be done here.
Dimensions can have multiple values for TSV and CSV data. To specify the delimiter for a multi-value dimension, set the listDelimiter in the parseSpec.
JSON data can contain multi-value dimensions as well. The multiple values for a dimension must be formatted as a JSON array in the ingested data. No additional parseSpec configuration is needed.